Purification and kinetic characterization of polyphenol oxidase from Barbados cherry (Malpighia glabra L.)

Polyphenol oxidase (PPO) of Barbados cherry was extracted and purified through ammonium sulfate precipitation, gel filtration, and affinity chromatography. The purification factor for PPO was 60% with 8.3% yield. The enzyme was characterized for thermal stability, pH and kinetic parameters. The molecular mass of PPO was approximately the sum of 52 and 38 kDa estimated by SDS–PAGE. The purity was checked by native PAGE, showing a single prominent band. The optimum pH was 7.2. The enzyme had a temperature optimum at 40 °C and was relatively stable at 60 °C, with 55% loss of activity. Sodium diethyl dithiocarbamate (SDDC), l-cysteine and ascorbate significantly inhibited PPO activity. 4-Methyl catechol and catechol were found to be efficient diphenolic substrates for cherry PPO, considering the _Vmax/_Km$V_{\text{max}}/K_{\mathrm{m}}$ ratio. The data obtained in this study may help to understand cherry fruit browning.     

Dark fermentative biohydrogen production by Acinetobacter junii-AH4 utilizing various industry wastewaters

Hydrogen (H₂) is one of the most promising renewable energy sources, anaerobic bacterial H₂ fermentation is considered as one of the most environmentally sustainable alternatives to meet the potential fossil fuel demand. Bio-H₂ is the cleanest and most effective source of energy provided by the dark fermentation utilizing organic substrates and different wastewaters. In this study, the bio-H₂ production was achieved by using the bacteria Acinetobacter junii-AH4. Further, optimization was carried out at different pH (5.0–8.0) in the presence of wastewaters as substrates (Rice mill wastewater (RMWW), Food wastewater (FWW) and Sugar wastewater (SWW). In this way, the optimized experiments excelled with the maximum cumulative H₂ production of 566.44 ± 3.5 mL/L (100% FWW at pH 7.5) in the presence of Acinetobacter junii-AH4. To achieve this, a bioreactor (3 L) was employed for the effective production of H₂ and Acinetobacter junii-AH4 has shown the highest cumulative H₂ of 613.2 ± 3.0 mL/L, HPR of 8.5 ± 0.4 mL/L/h, HY of 1.8 ± 0.09 mol H₂/mol glucose. Altogether, the present study showed a COD removal efficiency of 79.9 ± 3.5% by utilizing 100% food wastewater at pH 7.5. The modeled data established a batch fermentation system for sustainable H₂ production. This study has aided to achieve an ecofriendly approach using specific wastewaters for the production of bio-H₂.     

Cluster head selection for energy efficient and delay-less routing in wireless sensor network

Wireless sensor network (WSN) is comprised of tiny, cheap and power-efficient sensor nodes which effectively transmit data to the base station. The main challenge of WSN is the distance, energy and time delay. The power resource of the sensor node is a non-rechargeable battery. Here the greater the distance between the nodes, higher the energy consumption. For having the effective transmission of data with less energy, the cluster-head approach is used. It is well known that the time delay is directly proportional to the distance between the nodes and the base station. The cluster head is selected in such a way that it is spatially closer enough to the base station as well as the sensor nodes. So, the time delay can be substantially reduced. This, in turn, the transmission speed of the data packets can be increased. Firefly algorithm is developed for maximizing the energy efficiency of network and lifetime of nodes by selecting the cluster head optimally. In this paper firefly with cyclic randomization is proposed for selecting the best cluster head. The network performance is increased in this method when compared to the other conventional algorithms.

     

An Efficient Image Analysis Framework for the Classification of Glioma Brain Images Using CNN Approach

The identification of brain tumors is multifarious work for the separation of the similar intensity pixels from their surrounding neighbours. The detection of tumors is performed with the help of automatic computing technique as presented in the proposed work. The non-active cells in brain region are known to be benign and they will never cause the death of the patient. These non-active cells follow a uniform pattern in brain and have lower density than the surrounding pixels. The Magnetic Resonance (MR) image contrast is improved by the cost map construction technique. The deep learning algorithm for differentiating the normal brain MRI images from glioma cases is implemented in the proposed method. This technique permits to extract the linear features from the brain MR image and glioma tumors are detected based on these extracted features. Using k-mean clustering algorithm the tumor regions in glioma are classified. The proposed algorithm provides high sensitivity, specificity and tumor segmentation accuracy.     

Region based coronary artery segmentation using modified Frangi's vesselness measure

Recent research suggests that the cardiovascular diseases (CVDs), seem to be the foremost cause of mortality among the world populace. Three dimensional (3D) imaging modality such as computed tomography angiography(CTA) is a standard noninvasive imaging modality which has great potentials for the visualization of heart and coronary arteries. This article presents a fully automated method for coronary artery extraction using modified Frangi's vesselness measure and region based segmentation. In this article, grayness and gradient based measures are used while computing Frangi's vesselness measure to improve the extraction of coronary arteries. The obtained vesselness measures are utilized for automatically computing the location of ostia. The locations of ostia are then used as starting seed points in region growing segmentation to extract coronary arteries. Three major coronary arteries, namely the left anterior descending artery (LAD), left circumflex artery (LCX) and right coronary artery (RCA) are segmented using the proposed method and the centerlines are extracted for the main coronary branches. The performance of the proposed method is evaluated using 12 3D CCTA data set. The experimental results reveal that during the calculation of modified Frangi's vesselness measure the proposed method gives improved results. The qualitative results obtained during the segmentation stage are also convincing. The average segmentation accuracy and overlap measure of the proposed method are 97.4% and 77.86%, respectively. Hence, the proposed automated approach can detect and extract coronary arteries in CCTA images with high performance.     

Influence of Axial Force on Tensile Strength and Microstructural Characteristics of Friction Stir Buttwelded Aluminum Alloy/Steel Joints

Strength of Materials - In this work, the effect of axial force on the tensile strength, microhardness, joint interface microstructure and fracture surface morphology of friction stir welded (FSW)...     

Analysis and classification of malignancy in pancreatic magnetic resonance images using neural network techniques

Computer-aided diagnosis (CAD) is a computerized way of detecting tumors in MR images. Magnetic resonance imaging (MRI) has been generally used in the diagnosis and detection of pancreatic tumors. In a medical imaging system, soft tissue contrast and noninvasiveness are clear preferences of MRI. Inaccurate detection of tumor and long time consumption are the disadvantages of MRI. Computerized classifiers can greatly renew the diagnosis activity, in terms of both accuracy and time necessity by normal and abnormal images, automatically. This article presents an intelligent, automatic, accurate, and robust method to classify human pancreas MRI images as normal or abnormal in terms of pancreatic tumor. It represents the response of artificial neural network (ANN) and support vector machine (SVM) techniques for pancreatic tumor classification. For this, we extract features from MR images of pancreas using the GLCM method and select the best features using JAFER algorithm. These features are analyzed by five classification techniques: ANN BP, ANN RBF, SVM Linear, SVM Poly, and SVM RBF. We compare the results with benchmark data set of MR brain images. The analytical outcome presents that the two best features used to classify the MR images using ANN BP technique have 98% classification accuracy.     

Miniaturization of monopole antenna using high refractive index metamaterial loading

In this article, miniaturization of the wire monopole antenna with the help of high refractive index (HRI) metamaterial is presented. For the first time, HRI medium is realized by using the array of single ring split ring resonator. By surrounding the wire monopole with the array of SR‐SRRs, the effective wavelength is squeezed in the vicinity of the near field dominance. By the loading of the monopole with such an HRI medium, the size of the antenna is reduced from 29.5 to 16 mm, without any deformation in the current distribution and radiation pattern corresponding to the fundamental resonance. The simulated and measured results are agreed to the same.